From basic concepts to emerging technologies in regional anesthesia
Bibliographic record
Abstract
PURPOSE OF REVIEW: The present article details how our understanding of the basic concepts of regional anesthesia has recently evolved. We will appraise current technological advances and question the commensurate nature of the relationship between tradition and innovation. RECENT FINDINGS: Ultrasound localization has enhanced our understanding of the needle-nerve relationship. Intraneural injection of local anesthetic may occur with greater frequency than previously thought without inevitably leading to neurological complications. The ratio of neural to non-neural tissue varies both between and within nerves and may be an important determinant of neural injury. Ultrasonographic evidence of intraneural injection is subject to observer expertise and the resolution of the ultrasound image. Current ultrasound resolution capability does not reliably permit differentiation between intrafascicular and extrafascicular drug injection. Perineural electrical impedance may be a determinant of current threshold and conceivably distinguish between intraneural and extraneural tissue. Technology that enhances the sonographic image of both procedure needle and target nerve is the focus of current endeavors in ultrasound innovation.There is inconclusive evidence that the use of ultrasound technology has reduced the incidence of local anesthetic toxicity. Lipid emulsion therapy is now an accepted treatment for systemic local anesthetic toxicity. There are new reports on the development of an ultra long-acting local anesthetic agent that would permit lower doses and superannuate catheter-based continuous regional anesthesia techniques. SUMMARY: Over the past decade, our understanding of the fundamental concepts of regional anesthesia continues to be challenged by emerging experimental and clinical evidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".